Overview

Dataset statistics

Number of variables17
Number of observations120
Missing cells166
Missing cells (%)8.1%
Duplicate rows4
Duplicate rows (%)3.3%
Total size in memory16.1 KiB
Average record size in memory137.1 B

Variable types

NUM16
DATE1

Warnings

Dataset has 4 (3.3%) duplicate rows Duplicates
RDPI is highly correlated with Years and 1 other fieldsHigh correlation
Years is highly correlated with RDPI and 1 other fieldsHigh correlation
Pop is highly correlated with Years and 1 other fieldsHigh correlation
Wings_P is highly correlated with Comm FS Wing PriceHigh correlation
Comm FS Wing Price is highly correlated with Wings_PHigh correlation
Years has 70 (58.3%) missing values Missing
Time Series has 6 (5.0%) missing values Missing
RDPI has 7 (5.8%) missing values Missing
Retail Fresh Wing Sales has 6 (5.0%) missing values Missing
Retail Fresh Wing Price has 6 (5.0%) missing values Missing
Comm FS Wing Sales has 6 (5.0%) missing values Missing
Comm FS Wing Price has 6 (5.0%) missing values Missing
FS Nuggets Servings has 6 (5.0%) missing values Missing
Unemployment has 6 (5.0%) missing values Missing
Traffic has 6 (5.0%) missing values Missing
Pop has 7 (5.8%) missing values Missing
Wings_P has 5 (4.2%) missing values Missing
Operator Count has 6 (5.0%) missing values Missing
Wing Servings has 6 (5.0%) missing values Missing
Retail Store Count has 6 (5.0%) missing values Missing
Retail Wing Promotion has 6 (5.0%) missing values Missing
Wing Inventory has 5 (4.2%) missing values Missing
Time Series is uniformly distributed Uniform

Reproduction

Analysis started2020-11-04 18:59:20.861530
Analysis finished2020-11-04 19:00:36.609132
Duration1 minute and 15.75 seconds
Software versionpandas-profiling v2.9.0
Download configurationconfig.yaml

Variables

Years
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct10
Distinct (%)20.0%
Missing70
Missing (%)58.3%
Infinite0
Infinite (%)0.0%
Mean2015.5
Minimum2011
Maximum2020
Zeros0
Zeros (%)0.0%
Memory size960.0 B
2020-11-05T00:30:36.919397image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum2011
5-th percentile2011
Q12013
median2015.5
Q32018
95-th percentile2020
Maximum2020
Range9
Interquartile range (IQR)5

Descriptive statistics

Standard deviation2.901442287
Coefficient of variation (CV)0.001439564519
Kurtosis-1.225789813
Mean2015.5
Median Absolute Deviation (MAD)2.5
Skewness0
Sum100775
Variance8.418367347
MonotocityNot monotonic
2020-11-05T00:30:37.295274image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
201754.2%
 
201554.2%
 
201354.2%
 
202054.2%
 
201954.2%
 
201854.2%
 
201654.2%
 
201454.2%
 
201254.2%
 
201154.2%
 
(Missing)7058.3%
 
ValueCountFrequency (%) 
201154.2%
 
201254.2%
 
201354.2%
 
201454.2%
 
201554.2%
 
ValueCountFrequency (%) 
202054.2%
 
201954.2%
 
201854.2%
 
201754.2%
 
201654.2%
 

Time Series
Date

MISSING
UNIFORM

Distinct114
Distinct (%)100.0%
Missing6
Missing (%)5.0%
Memory size960.0 B
Minimum2011-01-01 00:00:00
Maximum2020-06-01 00:00:00
2020-11-05T00:30:37.764576image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-05T00:30:38.301576image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

RDPI
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct112
Distinct (%)99.1%
Missing7
Missing (%)5.8%
Infinite0
Infinite (%)0.0%
Mean41896.69027
Minimum38610
Maximum51550
Zeros0
Zeros (%)0.0%
Memory size960.0 B
2020-11-05T00:30:38.839299image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum38610
5-th percentile38725.2
Q139483
median41809
Q343973
95-th percentile45831.8
Maximum51550
Range12940
Interquartile range (IQR)4490

Descriptive statistics

Standard deviation2610.191793
Coefficient of variation (CV)0.06230066807
Kurtosis0.4311734026
Mean41896.69027
Median Absolute Deviation (MAD)2292
Skewness0.7338080619
Sum4734326
Variance6813101.198
MonotocityNot monotonic
2020-11-05T00:30:39.574519image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
4188321.7%
 
4555510.8%
 
4420810.8%
 
3867410.8%
 
3903010.8%
 
4549510.8%
 
4470910.8%
 
4461210.8%
 
4180910.8%
 
4206010.8%
 
Other values (102)10285.0%
 
(Missing)75.8%
 
ValueCountFrequency (%) 
3861010.8%
 
3867210.8%
 
3867410.8%
 
3868710.8%
 
3870210.8%
 
ValueCountFrequency (%) 
5155010.8%
 
4897110.8%
 
4625210.8%
 
4606110.8%
 
4591910.8%
 

Retail Fresh Wing Sales
Real number (ℝ≥0)

MISSING

Distinct114
Distinct (%)100.0%
Missing6
Missing (%)5.0%
Infinite0
Infinite (%)0.0%
Mean22234394.59
Minimum12133564
Maximum42564371.19
Zeros0
Zeros (%)0.0%
Memory size960.0 B
2020-11-05T00:30:39.944707image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum12133564
5-th percentile13968653.2
Q117869948.5
median22323412.86
Q324861131.86
95-th percentile31582916.99
Maximum42564371.19
Range30430807.19
Interquartile range (IQR)6991183.358

Descriptive statistics

Standard deviation5522404.148
Coefficient of variation (CV)0.2483721392
Kurtosis1.085323037
Mean22234394.59
Median Absolute Deviation (MAD)3675766.86
Skewness0.7162443776
Sum2534720983
Variance3.049694757e+13
MonotocityNot monotonic
2020-11-05T00:30:40.354693image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
1720556410.8%
 
1652039310.8%
 
2167852610.8%
 
1653280110.8%
 
2394800610.8%
 
20081886.6910.8%
 
42564371.1910.8%
 
30201308.3310.8%
 
1630360210.8%
 
1631182210.8%
 
Other values (104)10486.7%
 
(Missing)65.0%
 
ValueCountFrequency (%) 
1213356410.8%
 
1226042910.8%
 
1251219410.8%
 
1317559310.8%
 
1338159210.8%
 
ValueCountFrequency (%) 
42564371.1910.8%
 
37589445.2910.8%
 
35364843.810.8%
 
34524711.8910.8%
 
32516686.2610.8%
 

Retail Fresh Wing Price
Real number (ℝ≥0)

MISSING

Distinct114
Distinct (%)100.0%
Missing6
Missing (%)5.0%
Infinite0
Infinite (%)0.0%
Mean2.644355497
Minimum2.219204819
Maximum2.973794947
Zeros0
Zeros (%)0.0%
Memory size960.0 B
2020-11-05T00:30:40.787012image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum2.219204819
5-th percentile2.279166904
Q12.593145863
median2.696189556
Q32.769318685
95-th percentile2.851153875
Maximum2.973794947
Range0.754590128
Interquartile range (IQR)0.1761728215

Descriptive statistics

Standard deviation0.1767973237
Coefficient of variation (CV)0.06685837962
Kurtosis0.1034454822
Mean2.644355497
Median Absolute Deviation (MAD)0.07740988441
Skewness-0.9387608251
Sum301.4565267
Variance0.03125729366
MonotocityNot monotonic
2020-11-05T00:30:41.200470image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
2.59373129110.8%
 
2.8665055110.8%
 
2.24409684910.8%
 
2.80089248410.8%
 
2.57551421910.8%
 
2.57427655610.8%
 
2.74103643110.8%
 
2.30148318510.8%
 
2.83705024310.8%
 
2.38088157610.8%
 
Other values (104)10486.7%
 
(Missing)65.0%
 
ValueCountFrequency (%) 
2.21920481910.8%
 
2.23254863110.8%
 
2.23936674110.8%
 
2.24349141810.8%
 
2.24409684910.8%
 
ValueCountFrequency (%) 
2.97379494710.8%
 
2.96278284710.8%
 
2.86925459610.8%
 
2.8665055110.8%
 
2.86309019610.8%
 

Comm FS Wing Sales
Real number (ℝ≥0)

MISSING

Distinct114
Distinct (%)100.0%
Missing6
Missing (%)5.0%
Infinite0
Infinite (%)0.0%
Mean42919868.72
Minimum33057754.56
Maximum61182491
Zeros0
Zeros (%)0.0%
Memory size960.0 B
2020-11-05T00:30:41.584743image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum33057754.56
5-th percentile33954645.4
Q137457299.8
median42427490.45
Q347081639.28
95-th percentile56583288
Maximum61182491
Range28124736.44
Interquartile range (IQR)9624339.473

Descriptive statistics

Standard deviation6778457.369
Coefficient of variation (CV)0.1579328542
Kurtosis0.04633351701
Mean42919868.72
Median Absolute Deviation (MAD)4928626.07
Skewness0.7365346295
Sum4892865034
Variance4.59474843e+13
MonotocityNot monotonic
2020-11-05T00:30:41.989197image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
47680571.7710.8%
 
35752242.6610.8%
 
38541215.9810.8%
 
5699441310.8%
 
39073280.5210.8%
 
6118249110.8%
 
4497876710.8%
 
6010133010.8%
 
3464068610.8%
 
49550968.5710.8%
 
Other values (104)10486.7%
 
(Missing)65.0%
 
ValueCountFrequency (%) 
33057754.5610.8%
 
33230430.210.8%
 
33373634.6810.8%
 
33751611.7110.8%
 
33833875.110.8%
 
ValueCountFrequency (%) 
6118249110.8%
 
6010133010.8%
 
6008674210.8%
 
5954202010.8%
 
5886125310.8%
 

Comm FS Wing Price
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct103
Distinct (%)90.4%
Missing6
Missing (%)5.0%
Infinite0
Infinite (%)0.0%
Mean1.939969407
Minimum1.12
Maximum2.519452521
Zeros0
Zeros (%)0.0%
Memory size960.0 B
2020-11-05T00:30:42.414271image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1.12
5-th percentile1.2895
Q11.824167969
median1.999121092
Q32.153538771
95-th percentile2.329268701
Maximum2.519452521
Range1.399452521
Interquartile range (IQR)0.3293708018

Descriptive statistics

Standard deviation0.3069138849
Coefficient of variation (CV)0.1582055283
Kurtosis0.383213076
Mean1.939969407
Median Absolute Deviation (MAD)0.166819735
Skewness-0.8157250488
Sum221.1565124
Variance0.09419613274
MonotocityNot monotonic
2020-11-05T00:30:42.787200image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
232.5%
 
1.7432.5%
 
1.5521.7%
 
1.5321.7%
 
1.5921.7%
 
1.8621.7%
 
1.1421.7%
 
1.8321.7%
 
2.1921.7%
 
1.95659048910.8%
 
Other values (93)9377.5%
 
(Missing)65.0%
 
ValueCountFrequency (%) 
1.1210.8%
 
1.1421.7%
 
1.2210.8%
 
1.2410.8%
 
1.2710.8%
 
ValueCountFrequency (%) 
2.51945252110.8%
 
2.51443010510.8%
 
2.45088991310.8%
 
2.41416803310.8%
 
2.39960871210.8%
 

FS Nuggets Servings
Real number (ℝ≥0)

MISSING

Distinct114
Distinct (%)100.0%
Missing6
Missing (%)5.0%
Infinite0
Infinite (%)0.0%
Mean175339.502
Minimum126984
Maximum245847
Zeros0
Zeros (%)0.0%
Memory size960.0 B
2020-11-05T00:30:43.151030image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum126984
5-th percentile141937.4043
Q1156059.5059
median168361
Q3191042.2229
95-th percentile225402.3
Maximum245847
Range118863
Interquartile range (IQR)34982.71703

Descriptive statistics

Standard deviation26253.99238
Coefficient of variation (CV)0.1497323312
Kurtosis-0.1603192917
Mean175339.502
Median Absolute Deviation (MAD)16013.84834
Skewness0.7033810335
Sum19988703.23
Variance689272116
MonotocityNot monotonic
2020-11-05T00:30:43.568795image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
165849.788710.8%
 
191138.963910.8%
 
16810410.8%
 
17045110.8%
 
16964610.8%
 
160260.08710.8%
 
143450.191310.8%
 
180308.20110.8%
 
19706110.8%
 
163134.592510.8%
 
Other values (104)10486.7%
 
(Missing)65.0%
 
ValueCountFrequency (%) 
12698410.8%
 
13073710.8%
 
137986.565310.8%
 
13958710.8%
 
140219.226110.8%
 
ValueCountFrequency (%) 
24584710.8%
 
24028610.8%
 
23756810.8%
 
23003310.8%
 
22834510.8%
 

Unemployment
Real number (ℝ≥0)

MISSING

Distinct49
Distinct (%)43.0%
Missing6
Missing (%)5.0%
Infinite0
Infinite (%)0.0%
Mean0.05981578947
Minimum0.035
Maximum0.147
Zeros0
Zeros (%)0.0%
Memory size960.0 B
2020-11-05T00:30:43.982936image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0.035
5-th percentile0.036
Q10.04125
median0.053
Q30.07675
95-th percentile0.09035
Maximum0.147
Range0.112
Interquartile range (IQR)0.0355

Descriptive statistics

Standard deviation0.02154664247
Coefficient of variation (CV)0.360216636
Kurtosis1.832807721
Mean0.05981578947
Median Absolute Deviation (MAD)0.014
Skewness1.157649941
Sum6.819
Variance0.0004642578016
MonotocityNot monotonic
2020-11-05T00:30:44.354518image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%) 
0.04165.0%
 
0.0965.0%
 
0.04965.0%
 
0.0565.0%
 
0.08254.2%
 
0.03754.2%
 
0.03843.3%
 
0.04443.3%
 
0.03643.3%
 
0.03943.3%
 
Other values (39)6453.3%
 
(Missing)65.0%
 
ValueCountFrequency (%) 
0.03543.3%
 
0.03643.3%
 
0.03754.2%
 
0.03843.3%
 
0.03943.3%
 
ValueCountFrequency (%) 
0.14710.8%
 
0.13310.8%
 
0.11110.8%
 
0.09210.8%
 
0.09121.7%
 

Traffic
Real number (ℝ≥0)

MISSING

Distinct114
Distinct (%)100.0%
Missing6
Missing (%)5.0%
Infinite0
Infinite (%)0.0%
Mean1423974.819
Minimum978249.75
Maximum1627768
Zeros0
Zeros (%)0.0%
Memory size960.0 B
2020-11-05T00:30:44.736840image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum978249.75
5-th percentile1282335.6
Q11390127.4
median1426168.125
Q31487755.688
95-th percentile1519096.85
Maximum1627768
Range649518.25
Interquartile range (IQR)97628.2875

Descriptive statistics

Standard deviation89635.9387
Coefficient of variation (CV)0.06294769929
Kurtosis5.865385513
Mean1423974.819
Median Absolute Deviation (MAD)48753.8
Skewness-1.532676047
Sum162333129.3
Variance8034601506
MonotocityNot monotonic
2020-11-05T00:30:45.161412image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
145612610.8%
 
1458816.510.8%
 
162164210.8%
 
148997810.8%
 
1387615.410.8%
 
1368218.510.8%
 
1498006.610.8%
 
1450302.810.8%
 
148293110.8%
 
1490612.7510.8%
 
Other values (104)10486.7%
 
(Missing)65.0%
 
ValueCountFrequency (%) 
978249.7510.8%
 
115250610.8%
 
1157706.410.8%
 
119120910.8%
 
122269810.8%
 
ValueCountFrequency (%) 
162776810.8%
 
162164210.8%
 
159957810.8%
 
157945210.8%
 
152290310.8%
 

Pop
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct113
Distinct (%)100.0%
Missing7
Missing (%)5.8%
Infinite0
Infinite (%)0.0%
Mean321065.5964
Minimum311023
Maximum329894
Zeros0
Zeros (%)0.0%
Memory size960.0 B
2020-11-05T00:30:45.587349image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum311023
5-th percentile311995
Q1316016
median321428
Q3326216
95-th percentile329240.2
Maximum329894
Range18871
Interquartile range (IQR)10200

Descriptive statistics

Standard deviation5701.335086
Coefficient of variation (CV)0.01775753974
Kurtosis-1.270420718
Mean321065.5964
Median Absolute Deviation (MAD)5023
Skewness-0.1368641876
Sum36280412.39
Variance32505221.76
MonotocityNot monotonic
2020-11-05T00:30:45.975044image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
31388310.8%
 
31990410.8%
 
31869210.8%
 
31568210.8%
 
32656310.8%
 
32005310.8%
 
32387610.8%
 
32976010.8%
 
31133310.8%
 
32814010.8%
 
Other values (103)10385.8%
 
(Missing)75.8%
 
ValueCountFrequency (%) 
31102310.8%
 
31117310.8%
 
31133310.8%
 
31150210.8%
 
31167810.8%
 
ValueCountFrequency (%) 
32989410.8%
 
32976010.8%
 
329673.389410.8%
 
32952710.8%
 
32942310.8%
 

Wings_P
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct108
Distinct (%)93.9%
Missing5
Missing (%)4.2%
Infinite0
Infinite (%)0.0%
Mean1.491407826
Minimum0.6745
Maximum2.1575
Zeros0
Zeros (%)0.0%
Memory size960.0 B
2020-11-05T00:30:46.411566image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0.6745
5-th percentile0.86814
Q11.3452
median1.5255
Q31.71395
95-th percentile1.90665
Maximum2.1575
Range1.483
Interquartile range (IQR)0.36875

Descriptive statistics

Standard deviation0.3118347904
Coefficient of variation (CV)0.2090875379
Kurtosis0.2363902023
Mean1.491407826
Median Absolute Deviation (MAD)0.1835
Skewness-0.6531093502
Sum171.5119
Variance0.09724093652
MonotocityNot monotonic
2020-11-05T00:30:46.957748image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
1.7643.3%
 
1.483621.7%
 
1.5721.7%
 
1.74521.7%
 
1.70921.7%
 
1.4310.8%
 
1.833310.8%
 
1.0810.8%
 
1.07910.8%
 
1.532410.8%
 
Other values (98)9881.7%
 
(Missing)54.2%
 
ValueCountFrequency (%) 
0.674510.8%
 
0.692610.8%
 
0.698110.8%
 
0.770510.8%
 
0.7910.8%
 
ValueCountFrequency (%) 
2.157510.8%
 
2.088310.8%
 
2.02510.8%
 
2.0210.8%
 
2.019110.8%
 

Operator Count
Real number (ℝ≥0)

MISSING

Distinct12
Distinct (%)10.5%
Missing6
Missing (%)5.0%
Infinite0
Infinite (%)0.0%
Mean658509.2451
Minimum642650.4469
Maximum664621
Zeros0
Zeros (%)0.0%
Memory size960.0 B
2020-11-05T00:30:47.403750image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum642650.4469
5-th percentile642650.4469
Q1649719.6018
median662389
Q3662982
95-th percentile664621
Maximum664621
Range21970.55313
Interquartile range (IQR)13262.39821

Descriptive statistics

Standard deviation7309.740559
Coefficient of variation (CV)0.01110043726
Kurtosis-0.4622901543
Mean658509.2451
Median Absolute Deviation (MAD)1804
Skewness-1.11259197
Sum75070053.94
Variance53432307.03
MonotocityNot monotonic
2020-11-05T00:30:47.704820image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%) 
6623891210.0%
 
6471491210.0%
 
6622361210.0%
 
6629821210.0%
 
6646211210.0%
 
6600041210.0%
 
6626791210.0%
 
6641931210.0%
 
649719.601886.7%
 
642650.446965.0%
 
Other values (2)43.3%
 
(Missing)65.0%
 
ValueCountFrequency (%) 
642650.446965.0%
 
642650.446910.8%
 
64623632.5%
 
6471491210.0%
 
649719.601886.7%
 
ValueCountFrequency (%) 
6646211210.0%
 
6641931210.0%
 
6629821210.0%
 
6626791210.0%
 
6623891210.0%
 

Wing Servings
Real number (ℝ≥0)

MISSING

Distinct114
Distinct (%)100.0%
Missing6
Missing (%)5.0%
Infinite0
Infinite (%)0.0%
Mean82332.91274
Minimum66810.10817
Maximum104885
Zeros0
Zeros (%)0.0%
Memory size960.0 B
2020-11-05T00:30:48.020827image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum66810.10817
5-th percentile68559.95
Q174806
median80177.54722
Q389828.7083
95-th percentile100069.1
Maximum104885
Range38074.89183
Interquartile range (IQR)15022.7083

Descriptive statistics

Standard deviation9880.760959
Coefficient of variation (CV)0.1200098555
Kurtosis-0.7179524718
Mean82332.91274
Median Absolute Deviation (MAD)6538
Skewness0.5012647841
Sum9385952.053
Variance97629437.13
MonotocityNot monotonic
2020-11-05T00:30:48.390991image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
10072310.8%
 
8033210.8%
 
8506510.8%
 
8459510.8%
 
8446210.8%
 
10164210.8%
 
94010.9163210.8%
 
9541910.8%
 
9309410.8%
 
7988310.8%
 
Other values (104)10486.7%
 
(Missing)65.0%
 
ValueCountFrequency (%) 
66810.1081710.8%
 
67422.7473310.8%
 
6771110.8%
 
6793910.8%
 
6794210.8%
 
ValueCountFrequency (%) 
10488510.8%
 
10483510.8%
 
10286810.8%
 
10177410.8%
 
10164210.8%
 

Retail Store Count
Real number (ℝ≥0)

MISSING

Distinct25
Distinct (%)21.9%
Missing6
Missing (%)5.0%
Infinite0
Infinite (%)0.0%
Mean58107.32557
Minimum55795.19294
Maximum60572.09836
Zeros0
Zeros (%)0.0%
Memory size960.0 B
2020-11-05T00:30:48.760044image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum55795.19294
5-th percentile55795.19294
Q157445.922
median57942.46206
Q358994.621
95-th percentile60572.09836
Maximum60572.09836
Range4776.90542
Interquartile range (IQR)1548.699

Descriptive statistics

Standard deviation1294.731269
Coefficient of variation (CV)0.0222817219
Kurtosis-0.2708559373
Mean58107.32557
Median Absolute Deviation (MAD)877.69106
Skewness0.1654232913
Sum6624235.115
Variance1676329.059
MonotocityNot monotonic
2020-11-05T00:30:49.204399image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%) 
55795.192941210.0%
 
59357.815361210.0%
 
60572.098361210.0%
 
57942.462061210.0%
 
56968.2892675.8%
 
57601.93832.5%
 
58098.97232.5%
 
57445.92232.5%
 
58131.04432.5%
 
58735.60432.5%
 
Other values (15)4436.7%
 
(Missing)65.0%
 
ValueCountFrequency (%) 
55795.192941210.0%
 
56968.2892621.7%
 
56968.2892675.8%
 
56982.69132.5%
 
57146.85132.5%
 
ValueCountFrequency (%) 
60572.098361210.0%
 
59357.815361210.0%
 
59083.65332.5%
 
58994.62132.5%
 
58985.81532.5%
 

Retail Wing Promotion
Real number (ℝ≥0)

MISSING

Distinct112
Distinct (%)98.2%
Missing6
Missing (%)5.0%
Infinite0
Infinite (%)0.0%
Mean3010.937065
Minimum602.75
Maximum7535.75
Zeros0
Zeros (%)0.0%
Memory size960.0 B
2020-11-05T00:30:49.588434image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum602.75
5-th percentile1511.375
Q12253.775
median2838.9
Q33712.9375
95-th percentile4823.6325
Maximum7535.75
Range6933
Interquartile range (IQR)1459.1625

Descriptive statistics

Standard deviation1143.321201
Coefficient of variation (CV)0.3797227163
Kurtosis1.257162946
Mean3010.937065
Median Absolute Deviation (MAD)696.375
Skewness0.7321398184
Sum343246.8254
Variance1307183.368
MonotocityNot monotonic
2020-11-05T00:30:49.997179image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
341821.7%
 
253021.7%
 
3441.7510.8%
 
2673.210.8%
 
4181.7510.8%
 
2725.2510.8%
 
1682.510.8%
 
392610.8%
 
237510.8%
 
359010.8%
 
Other values (102)10285.0%
 
(Missing)65.0%
 
ValueCountFrequency (%) 
602.7510.8%
 
79110.8%
 
1210.7510.8%
 
1222.510.8%
 
127510.8%
 
ValueCountFrequency (%) 
7535.7510.8%
 
5582.510.8%
 
5539.2510.8%
 
541810.8%
 
5212.510.8%
 

Wing Inventory
Real number (ℝ≥0)

MISSING

Distinct115
Distinct (%)100.0%
Missing5
Missing (%)4.2%
Infinite0
Infinite (%)0.0%
Mean67179579.67
Minimum91662.5
Maximum104951000
Zeros0
Zeros (%)0.0%
Memory size960.0 B
2020-11-05T00:30:50.506104image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum91662.5
5-th percentile41159800
Q158684500
median66719000
Q377643000
95-th percentile97353400
Maximum104951000
Range104859337.5
Interquartile range (IQR)18958500

Descriptive statistics

Standard deviation17133947.67
Coefficient of variation (CV)0.2550469615
Kurtosis1.254069509
Mean67179579.67
Median Absolute Deviation (MAD)10110000
Skewness-0.3076955652
Sum7725651662
Variance2.935721628e+14
MonotocityNot monotonic
2020-11-05T00:30:51.055050image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
10277200010.8%
 
7163900010.8%
 
4119400010.8%
 
7995500010.8%
 
3619700010.8%
 
7703200010.8%
 
6946200010.8%
 
8199300010.8%
 
8681300010.8%
 
4108000010.8%
 
Other values (105)10587.5%
 
(Missing)54.2%
 
ValueCountFrequency (%) 
91662.510.8%
 
3318300010.8%
 
3373200010.8%
 
3565700010.8%
 
3619700010.8%
 
ValueCountFrequency (%) 
10495100010.8%
 
10277200010.8%
 
10156400010.8%
 
9870800010.8%
 
9774500010.8%
 

Interactions

2020-11-05T00:29:29.541341image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-05T00:29:29.806448image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-05T00:29:30.099899image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-05T00:29:30.427898image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-05T00:29:30.725939image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-05T00:29:31.061966image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-05T00:29:31.420983image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-05T00:29:31.647022image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-05T00:29:31.851042image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-05T00:29:32.047544image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-05T00:29:32.232427image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-05T00:29:32.441616image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-05T00:29:32.665364image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-05T00:29:32.940099image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-05T00:29:33.190099image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-05T00:29:33.472100image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-05T00:29:33.728601image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-05T00:29:33.995599image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-05T00:29:34.273221image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-05T00:29:34.540847image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-05T00:29:34.799221image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-05T00:29:35.092774image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-05T00:29:35.363016image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-05T00:29:35.603887image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-05T00:29:35.817481image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-05T00:29:35.997563image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-05T00:29:36.174562image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-05T00:29:36.381703image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-05T00:29:36.558287image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-05T00:29:36.717812image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-05T00:29:36.874883image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-05T00:29:37.086067image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-05T00:29:37.297003image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-05T00:29:37.479048image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-05T00:29:37.665671image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-05T00:29:37.848622image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-05T00:29:38.010503image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-05T00:29:38.181633image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-05T00:29:38.351543image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-05T00:29:38.500687image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-05T00:29:38.761276image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-05T00:29:38.901877image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-05T00:29:39.044041image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-05T00:29:39.173697image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-05T00:29:39.309109image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-05T00:29:39.451136image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-05T00:29:39.591225image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-05T00:29:39.745278image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-05T00:29:39.947302image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-05T00:29:40.199891image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-05T00:29:40.403435image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-05T00:29:40.623609image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-05T00:29:40.808208image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-05T00:29:40.989790image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-05T00:29:41.169832image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-05T00:29:41.353565image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-05T00:29:41.551034image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-05T00:29:41.713557image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-05T00:29:41.899179image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-05T00:29:42.086282image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-05T00:29:42.266968image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-05T00:29:42.461713image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-05T00:29:42.598882image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-05T00:29:42.770911image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-05T00:29:42.942007image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-05T00:29:43.061073image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-05T00:29:43.191093image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-05T00:29:43.318243image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-05T00:29:43.457362image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-05T00:29:43.645389image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-05T00:29:43.909093image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-05T00:29:44.175852image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-05T00:29:44.419071image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-05T00:29:44.632340image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-05T00:29:44.844067image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-05T00:29:45.107775image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-05T00:29:45.251430image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-05T00:29:45.376040image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-05T00:29:45.525026image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-05T00:29:45.784135image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-05T00:29:46.024793image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-05T00:29:46.281386image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-05T00:29:46.578168image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-05T00:29:46.780464image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-05T00:29:47.025694image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-05T00:29:47.288619image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-05T00:29:47.449229image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-05T00:29:47.700636image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-05T00:29:48.016583image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-05T00:29:48.316145image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-05T00:29:48.595805image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-05T00:29:48.876671image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-05T00:29:49.148778image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-05T00:29:49.500364image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-05T00:29:49.812961image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-05T00:29:50.105486image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-05T00:29:50.346188image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-05T00:29:50.531224image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-05T00:29:50.748576image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-05T00:29:50.964523image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-05T00:29:51.202882image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-05T00:29:51.397687image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-05T00:29:51.597860image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-05T00:29:51.768461image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-05T00:29:51.983559image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-05T00:29:52.218251image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-05T00:29:52.387387image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-05T00:29:52.600229image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-05T00:29:52.792376image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-05T00:29:52.928754image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-05T00:29:53.077265image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-05T00:29:53.263994image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-05T00:29:53.419825image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-05T00:29:53.647391image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-05T00:29:53.909822image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-05T00:29:54.155126image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-05T00:29:54.339404image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-05T00:29:54.608181image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-05T00:29:54.839610image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-05T00:29:55.082153image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-05T00:29:55.413627image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-05T00:29:55.623163image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-05T00:29:55.935835image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-05T00:29:56.105007image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-05T00:29:56.233293image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-05T00:29:56.440078image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-05T00:29:56.614190image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-05T00:29:56.841393image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-05T00:29:56.989446image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-05T00:29:57.171712image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-05T00:29:57.433475image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-05T00:29:57.855602image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-05T00:29:58.170799image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-05T00:29:58.450392image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-05T00:29:58.717678image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-05T00:29:59.194009image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-05T00:29:59.487029image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-05T00:29:59.794126image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-05T00:30:00.078290image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-05T00:30:00.339423image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-05T00:30:00.566785image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-05T00:30:00.873887image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-05T00:30:01.191751image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-05T00:30:01.473759image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-05T00:30:01.796547image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-05T00:30:02.100951image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-05T00:30:02.458592image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-05T00:30:02.753017image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-05T00:30:03.056065image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-05T00:30:03.409983image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-05T00:30:03.775333image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-05T00:30:04.157172image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-05T00:30:04.516412image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-05T00:30:04.853868image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-05T00:30:05.118323image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-05T00:30:05.458740image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-05T00:30:05.849274image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-05T00:30:06.245777image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-05T00:30:06.587649image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-05T00:30:06.930326image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-05T00:30:07.211267image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-05T00:30:07.483069image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-05T00:30:07.822447image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-05T00:30:08.170879image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-05T00:30:08.567635image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-05T00:30:08.949180image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-05T00:30:09.336500image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-05T00:30:09.694136image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-05T00:30:10.027293image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-05T00:30:10.314738image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-05T00:30:10.713759image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-05T00:30:10.881572image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-05T00:30:11.155845image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-05T00:30:11.461543image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-05T00:30:11.609848image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-05T00:30:11.759796image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-05T00:30:11.911947image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-05T00:30:12.093972image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-05T00:30:12.330040image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-05T00:30:12.559354image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-05T00:30:12.762943image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-05T00:30:13.012580image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-05T00:30:13.286251image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-05T00:30:13.608105image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-05T00:30:13.832247image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-05T00:30:14.088235image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-05T00:30:14.390109image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-05T00:30:14.737638image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-05T00:30:14.972338image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-05T00:30:15.233098image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-05T00:30:15.500640image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-05T00:30:15.798360image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-05T00:30:16.015572image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-05T00:30:16.258667image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-05T00:30:16.575016image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-05T00:30:16.930625image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-05T00:30:17.287679image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-05T00:30:17.519591image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-05T00:30:18.322506image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-05T00:30:18.677299image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-05T00:30:18.892659image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-05T00:30:19.112636image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-05T00:30:19.340160image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-05T00:30:19.636462image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-05T00:30:19.974816image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-05T00:30:20.248703image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-05T00:30:20.423470image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-05T00:30:20.737359image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-05T00:30:21.107877image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-05T00:30:21.461375image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-05T00:30:21.708479image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-05T00:30:21.924655image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-05T00:30:22.138047image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-05T00:30:22.291390image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-05T00:30:22.457615image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-05T00:30:22.692956image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-05T00:30:22.881810image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-05T00:30:23.141538image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-05T00:30:23.294721image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-05T00:30:23.422864image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-05T00:30:23.578126image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-05T00:30:23.773512image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-05T00:30:23.913575image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-05T00:30:24.128497image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-05T00:30:24.355982image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-05T00:30:24.642425image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-05T00:30:24.941708image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-05T00:30:25.207687image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-05T00:30:25.410399image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-05T00:30:25.597861image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-05T00:30:25.782956image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-05T00:30:26.114944image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-05T00:30:26.316193image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-05T00:30:26.546505image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-05T00:30:26.907084image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-05T00:30:27.195933image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-05T00:30:27.465040image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-05T00:30:27.741775image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-05T00:30:27.980873image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-05T00:30:28.272617image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-05T00:30:28.564067image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-05T00:30:28.849852image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-05T00:30:29.155576image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-05T00:30:29.447377image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-05T00:30:29.739972image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-05T00:30:30.015514image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-05T00:30:30.276410image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-05T00:30:30.555022image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-05T00:30:30.806275image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-05T00:30:31.089764image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-05T00:30:31.423492image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-05T00:30:31.629079image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-05T00:30:31.896841image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-05T00:30:32.155464image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-05T00:30:32.411854image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-05T00:30:32.652254image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Correlations

2020-11-05T00:30:51.380183image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2020-11-05T00:30:51.926088image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2020-11-05T00:30:52.634874image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2020-11-05T00:30:53.577538image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2020-11-05T00:30:33.278253image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-05T00:30:34.072045image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-05T00:30:34.937531image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-05T00:30:36.156733image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Sample

First rows

YearsTime SeriesRDPIRetail Fresh Wing SalesRetail Fresh Wing PriceComm FS Wing SalesComm FS Wing PriceFS Nuggets ServingsUnemploymentTrafficPopWings_POperator CountWing ServingsRetail Store CountRetail Wing PromotionWing Inventory
02011.02011-01-0138826.013911084.02.2928483.660115e+071.31143806.1897740.0921478322.0311023.00.9181649719.60178783004.72861255795.192941517.566045000.0
12011.02011-02-0138903.015248423.02.3007643.397114e+071.22142981.7996340.0901522705.0311173.00.7705649719.60178780581.05022555795.192941790.071010000.0
22011.02011-03-0138764.016532801.02.2434914.333886e+071.14191222.6694220.0901263041.0311333.00.6926649719.60178790943.08631255795.192941570.067778000.0
32011.02011-04-0138672.013175593.02.2393673.375161e+071.12155780.6745210.0911579452.0311502.00.6745649719.60178779294.60066455795.192941682.569045000.0
42011.02011-05-0138610.014373178.02.2192053.392401e+071.14151707.1603750.0901599578.0311678.00.6981649719.60178772620.44844055795.192942196.065801000.0
5NaN2011-06-0138766.017205564.02.2537594.259801e+071.24205333.2061770.0911292725.0311872.00.7900649719.60178796898.01313355795.192942667.569410000.0
6NaN2011-07-0138854.012260429.02.3271123.403503e+071.27160260.0869860.0901627768.0312077.00.8435649719.60178777215.70558855795.192941787.572477000.0
7NaN2011-08-0138785.012512194.02.3014833.492238e+071.30165606.5137570.0901621642.0312292.00.8787649719.60178780023.09443555795.192942674.062412000.0
8NaN2011-09-0138687.017281550.02.2325494.364277e+071.41199929.6242730.0901222698.0312509.01.0176642650.44687291303.62585055795.192942530.051240000.0
9NaN2011-10-0138732.013381592.02.2440973.517219e+071.53152141.1518940.0881517154.0312708.01.1057642650.44687267422.74733355795.192942367.551019000.0

Last rows

YearsTime SeriesRDPIRetail Fresh Wing SalesRetail Fresh Wing PriceComm FS Wing SalesComm FS Wing PriceFS Nuggets ServingsUnemploymentTrafficPopWings_POperator CountWing ServingsRetail Store CountRetail Wing PromotionWing Inventory
1102020.02020-03-0145381.03.452471e+072.86650646098821.02.052753183428.00.0441157706.40329673.3894431.5255662236.084056.057955.5402398.6053678000.0
1112020.02020-04-0151550.04.256437e+072.82204334640686.01.666673156896.00.147978249.75329760.0000001.0233646236.068887.058098.972602.7553413000.0
1122020.02020-05-0148971.03.037545e+072.96278342856623.01.937144190752.00.1331152506.00329894.0000001.4860646236.076169.058098.972791.0051723000.0
113NaN2020-06-01NaN2.928170e+072.97379559542020.02.095691245847.00.1111293264.00NaN1.5059646236.0104885.058098.9721734.4053888000.0
114NaNNaTNaNNaNNaNNaNNaNNaNNaNNaNNaN1.6218NaNNaNNaNNaN91662.5
115NaNNaTNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
116NaNNaTNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
117NaNNaTNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
118NaNNaTNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
119NaNNaTNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN